Mobility has gathered researchers’ interest as mobile users change their locations, and the need for quality of service and big data transmission has increased. This increase in wireless mobile users and their activities in mobile ad-hoc networks (MANET) has also resulted in the growth of bandwidth allocation. Efficient planning and management of the resources can be done through mobility prediction. Determining mobility is about finding the maximum probability of the next location where a mobile user could travel among different mobile networks. Mobility probability determination increases the authenticity of daily paths a mobile user adopts, making data transmission and resource management efficient. After mobility probability determination, data transmission starts after an encounter among source and destinations nodes. If an encounter doesn’t occur at the predicted future location, the resources are consumed for the continuous location next location prediction. The resources include computational resources and energy consumed by these resources for mobility probability determination. Therefore, there is a need for encounter prediction to ensure data transmission to reduce this energy consumption. Different mobility prediction models exist, e.g., the Markov model uses random data to predict the next state/location of the user. Still, current research lacks work on encounter prediction in MANET. In this paper, we have proposed a new method of data distribution via encounter prediction (DDVEP) for the encounter prediction consisting of data mining techniques and sequential algorithms. This algorithm stores the locations sequentially visited in the daily routine and predicts the next encounter of multiple users. The case studies performed using the mobility traces from the individuals on a university campus showed that the accuracy of the encounters predicted by the proposed model was 5% higher than the existing Markov model for the encounters and future location prediction.